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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+language:
+  - en
+tags:
+  - substandard-falsified-medicines
+  - herbal-medicine
+  - traditional-medicine
+  - contamination
+  - adulteration
+  - synthetic
+  - sub-saharan-africa
+pretty_name: Herbal & Traditional Medicine Safety (SSA)
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: traditional_healer_practice
+    data_files: data/herbal_traditional_healer.csv
+    default: true
+  - config_name: herbal_retail_market
+    data_files: data/herbal_retail_market.csv
+  - config_name: hospital_herb_drug_interaction
+    data_files: data/herbal_hospital_interaction.csv
+---
+
+# Herbal & Traditional Medicine Safety in Sub-Saharan Africa
+
+## Abstract
+
+Synthetic dataset modelling herbal/traditional medicine safety including contamination, adulteration, herb-drug interactions, and health outcomes across three settings in SSA. ~80% of Africans use traditional medicine; heavy metal contamination, microbial hazards, and adulteration with synthetic drugs are major safety concerns.
+
+## Parameterization Evidence
+
+| Parameter | Value | Source | Year |
+| --- | --- | --- | --- |
+| Heavy metal contamination (Pb, Cd, Hg, As) in herbals | Contamination | Frontiers Pharmacol | 2020 |
+| Adulteration with synthetic drugs, pesticides, microbes | Adulteration | IntechOpen | 2018 |
+| Microbial and heavy metal contamination review | Safety | BMC Complement Med | 2023 |
+| Agranulocytosis, meningitis, multi-organ failure | Harm | PubMed 22843016 | 2012 |
+
+## Validation
+
+![Validation Report](validation_report.png)
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/herbal-traditional-medicine-safety", "traditional_healer_practice")
+```
+
+## References
+
+1. Frontiers Pharmacology. Heavy metal contamination in herbal medicines. 2020.
+2. IntechOpen. Toxicity and safety of herbal medicines in Africa. 2018.
+3. BMC Complement Med. Microbial and heavy metal contamination review. 2023.
+4. PubMed 22843016. Contamination and adulteration of HMPs. 2012.
+
+## Citation
+
+```bibtex
+@dataset{electricsheepafrica_herbal_traditional_medicine_safety_2025,
+  title={Herbal and Traditional Medicine Safety in Sub-Saharan Africa},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/electricsheepafrica/herbal-traditional-medicine-safety}
+}
+```
+
+## License
+
+CC-BY-4.0

generate_dataset.py

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+"""Generate synthetic herbal & traditional medicine safety dataset for SSA.
+
+Research-based parameterization:
+- Frontiers Pharmacology (2020): Heavy metal contamination in herbal
+  medicines; Pb, Cd, Hg, As detected across multiple countries.
+- IntechOpen: Adulteration with synthetic drugs, pesticides, microbes,
+  heavy metals; hepatotoxicity, nephrotoxicity major concerns.
+- BMC Complement Med (2023): Microbial and heavy metal contamination;
+  safety concerns linked to global herbal trade increase.
+- PubMed 22843016: Contamination/adulteration causes agranulocytosis,
+  meningitis, multi-organ failure, death.
+- WHO: ~80% of African population uses traditional medicine; regulation
+  inadequate in most SSA countries.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+SEED = 42
+N_PER_SCENARIO = 10_000
+
+YEAR_RANGE = np.arange(2010, 2025)
+YEAR_WEIGHTS = np.linspace(0.85, 1.3, len(YEAR_RANGE))
+YEAR_WEIGHTS = YEAR_WEIGHTS / YEAR_WEIGHTS.sum()
+
+SCENARIOS = {
+    "traditional_healer_practice": {
+        "setting_probs": {"rural_healer": 0.40, "urban_healer": 0.25,
+                          "traditional_market": 0.20, "community": 0.15},
+        "product_probs": {"herbal_decoction": 0.30, "herbal_powder": 0.20,
+                          "plant_extract": 0.15, "mixed_herbal": 0.15,
+                          "animal_product": 0.10, "mineral_preparation": 0.10},
+        "heavy_metal_contamination_pct": 0.30,
+        "microbial_contamination_pct": 0.35,
+        "adulteration_pct": 0.15,
+        "hepatotoxicity_pct": 0.08,
+        "regulated_pct": 0.05,
+        "concurrent_conventional_pct": 0.25,
+    },
+    "herbal_retail_market": {
+        "setting_probs": {"herbal_shop": 0.35, "pharmacy_herbal_section": 0.20,
+                          "open_market": 0.25, "online_seller": 0.20},
+        "product_probs": {"packaged_herbal": 0.30, "herbal_supplement": 0.20,
+                          "herbal_tea": 0.15, "herbal_capsule": 0.15,
+                          "topical_herbal": 0.10, "imported_herbal": 0.10},
+        "heavy_metal_contamination_pct": 0.20,
+        "microbial_contamination_pct": 0.25,
+        "adulteration_pct": 0.20,
+        "hepatotoxicity_pct": 0.05,
+        "regulated_pct": 0.15,
+        "concurrent_conventional_pct": 0.35,
+    },
+    "hospital_herb_drug_interaction": {
+        "setting_probs": {"hospital": 0.35, "primary_care": 0.25,
+                          "HIV_clinic": 0.20, "oncology_clinic": 0.20},
+        "product_probs": {"herbal_decoction": 0.20, "herbal_supplement": 0.20,
+                          "herbal_capsule": 0.15, "mixed_herbal": 0.15,
+                          "african_potato": 0.10, "sutherlandia": 0.08,
+                          "other_traditional": 0.12},
+        "heavy_metal_contamination_pct": 0.15,
+        "microbial_contamination_pct": 0.15,
+        "adulteration_pct": 0.10,
+        "hepatotoxicity_pct": 0.10,
+        "regulated_pct": 0.10,
+        "concurrent_conventional_pct": 0.70,
+    },
+}
+
+SCENARIO_FILES = {
+    "traditional_healer_practice": "herbal_traditional_healer.csv",
+    "herbal_retail_market": "herbal_retail_market.csv",
+    "hospital_herb_drug_interaction": "herbal_hospital_interaction.csv",
+}
+
+
+def _choice(rng, prob_map):
+    keys = list(prob_map.keys())
+    weights = np.array(list(prob_map.values()), dtype=float)
+    weights = weights / weights.sum()
+    return rng.choice(keys, p=weights)
+
+
+def _simulate_scenario(name, params, seed):
+    rng = np.random.default_rng(seed)
+    records = []
+
+    for idx in range(N_PER_SCENARIO):
+        year = int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS))
+        setting = _choice(rng, params["setting_probs"])
+        age = int(np.clip(rng.normal(38, 16), 1, 80))
+        sex = rng.choice(["male", "female"], p=[0.40, 0.60])
+        product_type = _choice(rng, params["product_probs"])
+
+        indication = rng.choice(["malaria_fever", "stomach_GI", "sexual_enhancement",
+                                  "HIV_immune_boost", "diabetes", "hypertension",
+                                  "pain_inflammation", "fertility", "skin_disease", "other"],
+                                 p=[0.15, 0.12, 0.10, 0.10, 0.08, 0.08,
+                                    0.12, 0.08, 0.07, 0.10])
+
+        # Contamination
+        heavy_metal = int(rng.random() < params["heavy_metal_contamination_pct"])
+        lead_detected = int(heavy_metal and rng.random() < 0.50)
+        mercury_detected = int(heavy_metal and rng.random() < 0.25)
+        arsenic_detected = int(heavy_metal and rng.random() < 0.20)
+        microbial = int(rng.random() < params["microbial_contamination_pct"])
+        aflatoxin = int(microbial and rng.random() < 0.15)
+        ecoli = int(microbial and rng.random() < 0.30)
+        salmonella = int(microbial and rng.random() < 0.10)
+
+        # Adulteration
+        adulterated = int(rng.random() < params["adulteration_pct"])
+        synthetic_drug_added = int(adulterated and rng.random() < 0.40)
+        steroid_added = int(adulterated and rng.random() < 0.20)
+        sildenafil_added = int(adulterated and indication == "sexual_enhancement" and rng.random() < 0.30)
+        pesticide_residue = int(rng.random() < 0.10)
+
+        # Health effects
+        any_contamination = int(heavy_metal or microbial or adulterated)
+        hepatotoxicity = int(rng.random() < params["hepatotoxicity_pct"])
+        nephrotoxicity = int(heavy_metal and rng.random() < 0.05)
+        gi_adverse = int(rng.random() < 0.08)
+        skin_reaction = int(rng.random() < 0.04)
+        hospitalisation = int((hepatotoxicity or nephrotoxicity) and rng.random() < 0.30)
+        death = int(hospitalisation and rng.random() < 0.05)
+
+        # Herb-drug interactions
+        concurrent_conventional = int(rng.random() < params["concurrent_conventional_pct"])
+        disclosed_to_doctor = int(concurrent_conventional and rng.random() < 0.20)
+        interaction_risk = int(concurrent_conventional and rng.random() < 0.15)
+        interaction_type = rng.choice(["CYP_inhibition", "CYP_induction", "pharmacodynamic",
+                                        "absorption", "unknown"],
+                                       p=[0.30, 0.20, 0.25, 0.15, 0.10]) if interaction_risk else "none"
+
+        # Regulation & quality
+        registered_product = int(rng.random() < params["regulated_pct"])
+        label_present = int(rng.random() < 0.30)
+        dosage_specified = int(label_present and rng.random() < 0.40)
+        quality_tested = int(rng.random() < 0.02)
+        traditional_healer_registered = int(rng.random() < 0.10)
+
+        any_adverse = int(hepatotoxicity or nephrotoxicity or gi_adverse or interaction_risk)
+
+        record = {
+            "record_id": f"{name[:3].upper()}-{idx:05d}",
+            "scenario": name,
+            "year": year,
+            "setting": setting,
+            "age": age,
+            "sex": sex,
+            "product_type": product_type,
+            "indication": indication,
+            "heavy_metal_contaminated": heavy_metal,
+            "lead_detected": lead_detected,
+            "mercury_detected": mercury_detected,
+            "microbial_contaminated": microbial,
+            "aflatoxin": aflatoxin,
+            "adulterated": adulterated,
+            "synthetic_drug_added": synthetic_drug_added,
+            "pesticide_residue": pesticide_residue,
+            "hepatotoxicity": hepatotoxicity,
+            "nephrotoxicity": nephrotoxicity,
+            "gi_adverse": gi_adverse,
+            "hospitalisation": hospitalisation,
+            "death": death,
+            "concurrent_conventional": concurrent_conventional,
+            "disclosed_to_doctor": disclosed_to_doctor,
+            "interaction_risk": interaction_risk,
+            "interaction_type": interaction_type,
+            "registered_product": registered_product,
+            "label_present": label_present,
+            "quality_tested": quality_tested,
+            "any_adverse": any_adverse,
+        }
+        records.append(record)
+
+    return pd.DataFrame(records)
+
+
+def main():
+    output_dir = Path("data")
+    output_dir.mkdir(parents=True, exist_ok=True)
+    for idx, (name, params) in enumerate(SCENARIOS.items()):
+        df = _simulate_scenario(name, params, SEED + idx * 211)
+        df.to_csv(output_dir / SCENARIO_FILES[name], index=False)
+        print(f"Saved {name} -> {SCENARIO_FILES[name]}")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+"""Validate synthetic herbal & traditional medicine safety dataset."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+SCENARIO_FILES = {
+    "traditional_healer_practice": "herbal_traditional_healer.csv",
+    "herbal_retail_market": "herbal_retail_market.csv",
+    "hospital_herb_drug_interaction": "herbal_hospital_interaction.csv",
+}
+
+COLORS = {"traditional_healer_practice": "#e6550d", "herbal_retail_market": "#756bb1", "hospital_herb_drug_interaction": "#31a354"}
+
+
+def load_data() -> pd.DataFrame:
+    frames = []
+    for scenario, filename in SCENARIO_FILES.items():
+        df = pd.read_csv(Path("data") / filename)
+        frames.append(df)
+    return pd.concat(frames, ignore_index=True)
+
+
+def plot_validation(df: pd.DataFrame, output_path: Path) -> None:
+    fig, axes = plt.subplots(4, 2, figsize=(14, 16))
+    axes = axes.flatten()
+
+    cont_cols = ["heavy_metal_contaminated", "microbial_contaminated", "adulterated", "pesticide_residue"]
+    cont = df.groupby("scenario")[cont_cols].mean() * 100
+    cont.plot(kind="bar", ax=axes[0])
+    axes[0].set_title("Contamination & Adulteration (%)")
+    axes[0].legend(fontsize=6)
+
+    health_cols = ["hepatotoxicity", "nephrotoxicity", "gi_adverse", "hospitalisation", "death"]
+    health = df.groupby("scenario")[health_cols].mean() * 100
+    health.plot(kind="bar", ax=axes[1])
+    axes[1].set_title("Health Outcomes (%)")
+    axes[1].legend(fontsize=6)
+
+    prod = df.groupby(["scenario", "product_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+    prod.unstack().plot(kind="bar", stacked=True, ax=axes[2])
+    axes[2].set_title("Product Type Distribution")
+    axes[2].legend(fontsize=5)
+
+    ind = df.groupby(["scenario", "indication"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+    ind.unstack().plot(kind="bar", stacked=True, ax=axes[3])
+    axes[3].set_title("Indication Distribution")
+    axes[3].legend(fontsize=4)
+
+    int_cols = ["concurrent_conventional", "disclosed_to_doctor", "interaction_risk"]
+    intc = df.groupby("scenario")[int_cols].mean() * 100
+    intc.plot(kind="bar", ax=axes[4])
+    axes[4].set_title("Herb-Drug Interaction Risk (%)")
+    axes[4].legend(fontsize=7)
+
+    hm_cols = ["lead_detected", "mercury_detected"]
+    hm = df.groupby("scenario")[hm_cols].mean() * 100
+    hm.plot(kind="bar", ax=axes[5])
+    axes[5].set_title("Heavy Metal Detection (%)")
+    axes[5].legend(fontsize=7)
+
+    reg_cols = ["registered_product", "label_present", "quality_tested"]
+    reg = df.groupby("scenario")[reg_cols].mean() * 100
+    reg.plot(kind="bar", ax=axes[6])
+    axes[6].set_title("Regulation & Quality (%)")
+    axes[6].legend(fontsize=7)
+
+    adul_cols = ["synthetic_drug_added"]
+    adul = df.groupby("scenario")[adul_cols].mean() * 100
+    adul.plot(kind="bar", ax=axes[7])
+    axes[7].set_title("Synthetic Drug Adulteration (%)")
+    axes[7].legend(fontsize=7)
+
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+def main() -> None:
+    df = load_data()
+    plot_validation(df, Path("validation_report.png"))
+    print("Saved validation_report.png")
+
+
+if __name__ == "__main__":
+    main()